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Maneesh Kumar

Researcher at Cardiff University

Publications -  119
Citations -  4973

Maneesh Kumar is an academic researcher from Cardiff University. The author has contributed to research in topics: Six Sigma & Lean manufacturing. The author has an hindex of 31, co-authored 101 publications receiving 4067 citations. Previous affiliations of Maneesh Kumar include London Business School & Edinburgh Napier University.

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Critical success factors and hurdles to Six Sigma implementation: the case of a UK manufacturing SME

TL;DR: In this paper, the authors report key findings of a Six Sigma survey conducted in a UK manufacturing SME with an objective of assessing the status of Six Sigma in the company, and conclude that SMEs can also benefit from implementing Six Sigma similar to their larger counterpart.
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Application of Six Sigma DMAIC methodology in a transactional environment

TL;DR: In this article, the authors investigate and streamline the communication and information system within an infrastructure support service company using Six Sigma methodology, and highlight a significant concern with regard to CIM within all the business units of the group.
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Synergetic effect of lean and green on innovation: a resource-based perspective

TL;DR: In this paper, the authors conducted twelve semi-structured interviews with cross-functional team involved in integrated lean and green practices in two manufacturing organizations in the UK, and found that cross-function collaboration and collaboration with suppliers are key to promote innovation within operations and supply chains.
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Status of Quality Management practices in manufacturing SMEs: a comparative study between Australia and the UK

TL;DR: In this paper, the status of quality management (QM) practices in the manufacturing small and medium-sized enterprises (SMEs) in Australia and the UK, and the intention was to identify the trends for adoption of advanced QM practices such as Lean and Six Sigma.
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Reinforcement learning for facilitating human-robot-interaction in manufacturing

TL;DR: A novel methodology is presented for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues.